Retrieval Process Research Articles

Rare case of assault with knife penetrating base of the skull – Retrieval and management

Addressing a penetrating injury in the maxillofacial region poses significant challenges for oral and maxilofacial surgeons due to the presence or critical structures. Damage to these structures can lead to varying degrees of impairment and a decreased quality of life for the patient. A 48-year-old individual sustained a stabbing injury to the left side of their face. The knife remained lodged inside for 7 h until they were admitted to the trauma unit. The assailant had unsuccessfully attempted to remove the weapon by bending its handle. Following thorough examinations to pinpoint the exact location of the knife, it was safely removed under sedation. The patient experienced successful healing without major complications. The minimisation of scar tissue was achieved through proper debridement and layered linear wound closure. Subsequently, the patient was transferred to the general ward for monitoring and prescribed antibiotics to prevent wound infection. This report includes a detailed account of the weapon retrieval process and is management, along with a review of relevant literature Reconstruction of complicated facial wounds is difficult for surgeons, and early treatment is necessary to avoid additional harm.

Scientific, transparent and applicable rankings of Chinese pathological guidelines and consensus published in the medical journals in 2022

The STAR tool was used to evaluate and analyze the science, transparency, and applicability of Chinese pathology guidelines and consensus published in medical journals in 2022. There were a total of 18 pathology guidelines and consensuses published in 2022, including 1 guideline and 17 consensuses. The results showed that the guideline score was 21.83 points, lower than the overall guideline average (43.4 points). Consensus ratings scored an average of 27.87 points, on par with the overall consensus level (28.3 points). Areas that scored above the overall level were "conflict of interest" and "working groups", while areas that scored below the overall level were "proposals", "funding", "evidence", "consensus approaches" and "accessibility". To sum up, the formulation of pathology guidelines and consensuses in 2022 is not standardized, and the evidence retrieval process, evidence evaluation methods and grading criteria for recommendations on clinical issues are not provided in the formulation process; the process and method for reaching consensus are not provided, the plan is lacking, and registration is not carried out. It is therefore suggested that guidelines/consensus makers in the field of pathology should attach importance to evidence-based medical evidence, strictly follow guideline formulation methods and processes, further improve the scientific, applicable and transparent guidelines/consensuses in the field, and better provide support for clinicians and patients.

The Involvement of the Serotonergic System in Ketamine and Fluoxetine Combination-induced Cognitive Impairments in Mice.

Glutamatergic N-methyl-D-aspartate (NMDA) receptors play vital roles in memory formation. Changes in the activity of these receptors influence memory processes. Ketamine is a noncompetitive NMDA receptor antagonist drug with promising mood-altering and pain-reducing effects in low doses. These effects are believed to be related to altered serotonergic transmission. The present study investigated the involvement of the serotonergic system in low-dose ketamine administrations' effects on memory acquisition, consolidation, and retrieval processes. Sixty-four male BALB/c mice were used in this experiment and separated into 8t groups. Mice were treated subchronically with a selective serotonin reuptake inhibitor, fluoxetine, and a serotonin depletion agent, p-chlorophenylalanine (pCPA). A serotonin antagonist, methiothepin, and ketamine were acutely administered 60 minutes before or after the behavioral tests. A passive avoidance (PA) test measured emotional memory acquisition, consolidation, and retrieval processes. Hippocampi malondialdehyde (MDA) levels were analyzed, and histopathological examinations were performed. Ketamine alone did not significantly affect memory encoding processes in the PA test, while the ketamine-fluoxetine combination disrupted memory consolidation. Fluoxetine negatively affected the memory acquisition process, which was normalized during the consolidation and retrieval trials. Drug applications did not significantly alter hippocampal MDA levels. In all ketamine-applied groups, histopathologic alterations were evident. Low-dose ketamine administration induces neurodegeneration, and it also impairs memory functions when combined with fluoxetine, indicating increased serotonergic transmission may be involved in the memory-impairing and neurotoxic effects of ketamine.

Meta-analysis of TPACK-based Discovery Learning Model on Learning Outcomes in Elementary School Students

This study aims to determine the effect of TPACK-based discovery learning model on the learning outcomes of elementary school students. The source of data in this study comes from an analysis of 15 national journals published in 2022-2024. The process of data retrieval through the databases of google scholar, ScienceDirect and ERIC Journal. Data selection using PRISMA method in 2020. The inclusion criteria in this study are that the research must be experimental methods or quasi-experiments; research sample of elementary school students; research should be related to TPACK-based dsicovery learning models on student learning outcomes; learning materials related to mathematics and Indonesian; journals must be indexed by Science Technology Index (SINTA) and Garuda; and a sample size of > 20 students. Data analysis in this study is quantitative analysis by calculating the value of effect size assisted by JSAP application Version 0.8.5. The results concluded that the TPACK-based inquiry model had a significant influence on student learning outcomes with rES = 1,114; P < 0.001. This finding has a positive impact on PGSD teachers to implement this model in the learning process.

A Systematic Review of the Relationship between Social Isolation and Physical Health in Adults.

Background: According to the World Health Organization, social isolation, particularly of older adults, is a public health issue endangering the well-being of individuals, families, and communities. Social isolation affects health through biological, behavioral, and psychological pathways and is associated with physical and psychological/emotional well-being, increases morbidity and mortality rates, and lowers quality of life. Purpose: This systematic review examined the relationship between social isolation and physical health, including subjective and objective dimensions, and factors that influence this relationship in adults. Methods: This systematic review examined six electronic databases covering the field of health and human services and included results from 1 January 2017 to 10 March 2023 with key terms including adult social connection or social isolation coupled with health, physical, psychological, emotional, mental, or behavioral. The initial search yielded 925 research articles across all databases and was narrowed to 710 when the decision was made to focus on social isolation and physical health. Covidence was used throughout the retrieval and appraisal process, as provided in a PRISMA flow diagram. Twenty-four studies that scored 90 or above in the appraisal process were included in the systematic review. Results: The studies represented included seven studies conducted in the United States and seventeen studies conducted internationally. Regarding study design, twenty-three studies were quantitative, one was qualitative, and one was mixed methods. The majority of quantitative studies were correlational in design with nine being longitudinal. The majority of studies were based on large national data sets representing in total 298,653 participants aged 50 and older. The results indicate that social isolation is related to increases in inflammatory biomarkers associated with diseases, all-cause mortality, lower expectations of longevity, and frailty. In addition, social isolation was associated with cognitive decline and disruptions in sleep. Poor oral health increased social isolation. The results further indicated that decreased physical performance/function and a decline in physical activity were associated with social isolation, as well as decreased overall physical health, poor health behaviors, and self-care, and decreased health-related quality of life. Further research is warranted to examine the possible bidirectionality of these relationships and possible mediating, moderating, or confounding variables. Implications: Future research is needed to explore the biological and behavioral pathways in which social isolation negatively impacts physical health. Going forward, studies are needed that move beyond descriptive, exploratory methods and integrate data from qualitative and mixed-method designs that will inform the development and testing of a conceptual framework related to social isolation and health. By advancing the science behind social isolation, comprehensive interventions can be identified and tested with implications at the individual, family, community, and societal levels to reduce social isolation, particularly among adults, and improve health and quality of life.

The HR Insight Sidebar Extension for LinkedIn Profile

This paper explores the "HR Insights Sidebar Extension," which enhances LinkedIn profile evaluation through a chatbot, aiding informed decision-making with real-time analysis. AI-driven algorithms extract key information like skills, experience, and endorsements, generating concise summaries to streamline evaluations. Leveraging the OpenAI Assistant API's Information Retrieval and Natural Language Processing, the system extracts data from various file types. Users query the chatbot via a natural language interface, receiving direct answers and saving time. Tools like ChatGPT and Proxy curl improve profile evaluation efficiency and effectiveness for students, researchers, and professionals. Keywords- large language models, GPT-3 summarization, proxy curl, implementation, and integration.

HOW TO IMPACT WITH LIBRARY AND LIBRARIANSHIP INFORMATION RETRIEVAL & KNOWLEDGE

The impact of information retrieval and knowledge management in library and librarianship has evolved significantly with the advent of digital technologies and modern information systems. This study investigates the key role that libraries and librarians play in enhancing information retrieval processes and knowledge dissemination in academic, research, and public settings. It highlights the transformation of libraries from traditional repositories of books to dynamic centers for knowledge access and creation. The paper explores how advancements in digital libraries, metadata management, and search algorithms have enabled more efficient retrieval of information, thus empowering researchers, students, and the public. Furthermore, the research examines the skills and evolving role of librarians as key facilitators of knowledge management, curating and guiding users through vast resources. Challenges such as the digital divide, limited resources, and the need for ongoing professional development are also considered. The study concludes with recommendations for the future of librarianship, emphasizing the importance of adopting emerging technologies and continuing to support open access initiatives for better knowledge sharing.

Intelligent privacy‐preserving data management framework for medicine supply chain system

AbstractIn today's era, the pharmaceutical industry has integrated blockchain to secure the sensitive information of medicines, wherein public and private blockchains are used to preserve the security and privacy of the medicine supply chain data. However, conventional blockchains often limit scalability due to limited storage. Moreover, blockchain has loopholes; for example, it is not able to prove the validity of the data prior to being stored in the blockchain, which leads to fake data being added to the blockchain. As a result, it causes an issue of data provenance. Motivated by this, the proposed framework incorporated artificial intelligence (AI) algorithms to enhance the efficiency of the medicine supply chain data. The proposed framework integrated machine learning (ML) and blockchain, where ML classifies the valid and invalid data of the medicine supply chain, whereas blockchain stores only valid data and maintains its security and privacy. This identification helps the blockchain to verify medicine supply chain data before adding it to the blockchain. Additionally, we employed an InterPlanetary file system (IPFS) that saves medicine supply chain data and computes its hash to offer decentralized storage. Further, this hash data is stored on a private Hyperledger Fabric blockchain, which requires minimal storage instead of storing an entire large file. This minimal storage optimizes the process of data storage and retrieval in the Hyperledger Fabric blockchain, which enhances the scalability of the proposed framework. Finally, the result of the proposed framework is assessed in two phases: ML and blockchain, wherein the ML model's performance is measured by statistical measures and the blockchain‐based result is assessed using several performance parameters such as throughput is around (618 transactions per second), latency (0.12 s), response time (11 s) and data rate (282 Mbps).

Context-specific adaptation for head fakes in basketball: a study on player-specific fake-frequency schedules.

In basketball, an attacking player often plays a pass to one side while looking to the other side. This head fake provokes a conflict in the observing opponent, as the processing of the head orientation interferes with the processing of the pass direction. Accordingly, responses to passes with head fakes are slower and result in more errors than responses to passes without head fakes (head-fake effect). The head-fake effect and structurally similar interference effects (e.g., Stroop effect) are modulated by the frequency of conflicting trials. Previous studies mostly applied a block-wise manipulation of proportion congruency. However, in basketball (and also in other team sports), where different individual opponents can be encountered, it might be important to take the individual frequency (e.g., 20% vs. 80%) of these opponents into account. Therefore, the present study investigates the possibility to quickly (i.e., on a trial-by-trial basis) reconfigure the response behavior to different proportions of incongruent trials, which are contingent on different basketball players. Results point out that participants indeed adapted to the fake-frequency of different basketball players, which could be the result of strategic adaptation processes. Multi-level analyses, however, indicate that a substantial portion of the player-specific adaptation to fake frequencies is accounted by episodic retrieval processes, suggesting that item-specific proportion congruency effects can be explained in terms of stimulus-response binding and retrieval: The head orientation (e.g., to the right) of a current stimulus retrieves the last episode with the same head orientation including the response that was part of this last episode. Thus, from a theoretical perspective, an attacking player would provoke the strongest detrimental effect on an opponent if s/he repeats the same head movement but changes the direction of the pass. Whether it is at all possible to strategically apply this recommendation in practice needs still to be answered.

A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements

This article presents a method within a Bayesian framework for quantifying uncertainty in satellite aerosol remote sensing when retrieving aerosol optical depth (AOD). By using a Bayesian model averaging technique, we take into account uncertainty in aerosol optical model selection and also obtain a shared inference about AOD based on the best-fitting optical models. In particular, uncertainty caused by forward-model approximations has been taken into account in the AOD retrieval process to obtain a more realistic uncertainty estimate. We evaluated a model discrepancy, i.e., forward-model uncertainty, empirically by exploiting the residuals of model fits and using a Gaussian process to characterise the discrepancy. We illustrate the method with examples using observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite. We evaluated the results against ground-based remote sensing aerosol data from the Aerosol Robotic Network (AERONET).

Meaning as mentalization

The way we establish meaning has been a profound question not only in language research but in developmental science as well. The relation between linguistic form and content has been loosened up in recent pragmatic approaches to communication, showing that code-based models of language comprehension must be augmented by context-sensitive, pragmatic-inferential mechanisms to recover the speaker’s intended meaning. Language acquisition has traditionally been thought to involve building a mental lexicon and extracting syntactic rules from noisy linguistic input, while communicative-pragmatic inferences have also been argued to be indispensable. Recent research findings exploring the electrophysiological indicator of semantic processing, the N400, have raised serious questions about the traditional separation between semantic decoding and pragmatic inferential processes. The N400 appears to be sensitive to mentalization—the ability to attribute beliefs to social partners—already from its developmental onset. This finding raises the possibility that mentalization may not simply contribute to pragmatic inferences that enrich linguistic decoding processes but that the semantic system may be functioning in a fundamentally mentalistic manner. The present review first summarizes the key contributions of pragmatic models of communication to language comprehension. Then, it provides an overview of how communicative intentions are interpreted in developmental theories of communication, with a special emphasis on mentalization. Next, it discusses the sensitivity of infants to the information-transmitting potential of language, their ability to pick up its code-like features, and their capacity to track language comprehension of social partners using mentalization. In conclusion, I argue that the recovery of meaning during linguistic communication is not adequately modeled as a process of code-based semantic retrieval complemented by pragmatic inferences. Instead, the semantic system may establish meaning, as intended, during language comprehension and acquisition through mentalistic attribution of content to communicative partners.

Investing of Chatbots to Enhance the Library Services

Artificial Intelligence (AI) has become an indispensable tool in our daily lives and is a concept that refers to complex tasks that usually require significant human effort. Chatbots stand out as one of the primary applications of artificial intelligence, aiming to foster an interactive dialogue environment between humans and machines to address inquiries through textual or voice interfaces that simulate human cognition. Libraries can serve as an ideal environment for implementing this technology, where it can deliver advanced information services and streamline repetitive routine tasks typically performed by library staff. The paper discusses the applications of chatbots in automating library services, including reference services, user recommendations, information retrieval, data analysis, and other related processes, and their mechanism of action in processing natural language queries and responding to them accurately. Through a web survey, the study identified eighteen chatbot models that could be implemented in libraries. This survey provided details about these bots, including their type (free or commercial), ownership, release date, operating platforms, and key features. This allows libraries to choose from them. Integrating chatbots into libraries will open new horizons for enhancing their services and improving the user experience. Despite the benefits offered by these chatbots, they face several challenges and concerns regarding their usage, notably data privacy and security, and bias in retrieval. It is expected that in the future, these chatbots will become a fundamental component of providing library services.

Enhancing MRI image retrieval using autoencoder-based deep learning: A solution for efficient clinical and teaching applications

BackgroundMagnetic resonance imaging (MRI) image retrieval holds significant value in clinical contexts and medical education due to the shortcomings of traditional methods: slow speed, low accuracy, and limited learning capabilities. Improving this retrieval process is crucial for enhancing medical diagnostics and educational outcomes. Efforts to overcome these challenges are paramount for advancing healthcare practices and educational methodologies. MethodThis study explores the use of autoencoders in deep learning to effectively retrieve MRI images from databases for medical education, emphasizing the model's capacity to be trained with a small amount of labeled data. This work intends to improve the MRI image retrieval process by utilizing autoencoders, demonstrating the promise of deep learning technologies in medical image analysis without the need for large labeled datasets for training. ResultsResearch has demonstrated the exceptional advantages of this method for MRI image retrieval tasks, with an average accuracy of 99.09%. This indicates that the technique performs exceptionally well in this particular domain and is very effective and reliable in extracting MRI images. ConclusionsThis innovative approach can improve the archival management and diagnostic functions of medical images, providing an efficient and reliable solution for MRI image retrieval. It not only helps doctors with clinical diagnosis and medical teaching and research more quickly but also suggests a convenient solution for file management related to medical images.

Content Summarization and Question Answering System Using LLM

This paper presents a novel approach to content summarization, a pivotal task in information processing and knowledge extraction. Our methodology integrates cutting-edge technologies, including large language models (LLMs) and ad-vanced retrieval techniques, to efficiently distill extensive textual data into concise and informative summaries. By leveraging Anyscale LLMs for language comprehension and employing prompt engineering for model guidance, we propose a frame-work that prioritizes efficient document indexing and rapid retrieval using DuckDB vector stores. Additionally, we introduce the RetrievalQAWithSourcesChain framework, which combines LLMs with retrievers for proficient question answering. Em-pirical validation demonstrates the efficacy of our approach in generating high-quality content summaries with minimal manual intervention, thereby enhancing information retrieval and knowledge extraction processes. Index Terms—LLMs, Anyscale, prompt engineering, document indexing, DuckDB, vector stores, RetrievalQAWithSourcesChain framework, question answering, empirical validation

Overlapping neural correlates underpin theory of mind and semantic cognition: Evidence from a meta-analysis of 344 functional neuroimaging studies

Key unanswered questions for cognitive neuroscience include whether social cognition is underpinned by specialised brain regions and to what extent it simultaneously depends on more domain-general systems. Until we glean a better understanding of the full set of contributions made by various systems, theories of social cognition will remain fundamentally limited. In the present study, we evaluate a recent proposal that semantic cognition plays a crucial role in supporting social cognition. While previous brain-based investigations have focused on dissociating these two systems, our primary aim was to assess the degree to which the neural correlates are overlapping, particularly within two key regions, the anterior temporal lobe (ATL) and the temporoparietal junction (TPJ). We focus on activation associated with theory of mind (ToM) and adopt a meta-analytic activation likelihood approach to synthesise a large set of functional neuroimaging studies and compare their results with studies of semantic cognition. As a key consideration, we sought to account for methodological differences across the two sets of studies, including the fact that ToM studies tend to use nonverbal stimuli while the semantics literature is dominated by language-based tasks. Overall, we observed consistent overlap between the two sets of brain regions, especially in the ATL and TPJ. This supports the claim that tasks involving ToM draw upon more general semantic retrieval processes. We also identified activation specific to ToM in the right TPJ, bilateral anterior mPFC, and right precuneus. This is consistent with the view that, nested amongst more domain-general systems, there is specialised circuitry that is tuned to social processes.

Nasal inhalation does not improve memory of visual repetitions.

Several studies suggest that breathing entrains neural oscillations and thereby improves visual detection and memory performance during nasal inhalation. However, the evidence for this association is mixed, with some studies finding no, minor, or opposite effects. Here, we tested whether nasal breathing phase influences memory of repeated images presented in a rapid serial visual presentation (RSVP) task. The RSVP task is ideal for studying the effects of respiratory-entrained oscillations on visual memory because it engages critical aspects of sensory encoding that depend on oscillatory activity, such as fast processing of natural images, repetition detection, memory encoding, and retrieval. It also enables the presentation of a large number of stimuli during each phase of the breathing cycle. In two separate experiments (n = 72 and n = 142, respectively) where participants were explicitly asked to breathe through their nose, we found that nasal breathing phase at target presentation did not significantly affect memory performance. An exploratory analysis in the first experiment suggested a potential benefit for targets appearing approximately 1 s after inhalation. However, this finding was not replicated in the pre-registered second experiment with a larger sample. Thus, in two large sample experiments, we found no measurable impact of breathing phase on memory performance in the RSVP task. These results suggest that the natural breathing cycle does not have a significant impact on memory for repeated images and raise doubts about the idea that visual memory is broadly affected by the breathing phase.

A survey of neurosymbolic visual reasoning with scene graphs and common sense knowledge

A survey of neurosymbolic visual reasoning with scene graphs and common sense knowledge

A decentralized solid compound storage facility managed by a centralized electronic platform at a growing drug discovery company

Within a growing drug discovery company, scientists acquire (either through in house synthesis or purchase) then store, retrieve, and ship solid compound samples daily between multiple locations. The efficient management and tracking of this entire process to support drug discovery is a significant challenge. This article describes a decentralized and cost-effective inventory facility that simplifies the solid compound storage and retrieval process. Standardized storage cabinets from the market are utilized, providing a cost-effective physical infrastructure. The cabinets can be distributed across storage rooms at multiple sites and arranged into spaces with a variety of dimensions, allowing the system to be retrofitted into existing facilities and scaled up easily. We can provide storage close to work areas at each location, minimizing both unnecessary movement of staff and transportation of substances. We have applied a systematic barcoding method to the compound batch identifier that correlates with its compound location. This simplifies the compound registration process as well as the process of finding and returning compounds. Additionally, a centralized electronic platform has been employed to store, update and track solid compound information, such as properties, location and quantity. Compound shipment may be initiated from different sites, and a centralized electronic platform assists the information retrieval process, ensuring each location possesses up-to-date information. The electronic platform we present streamlines the management of compound registration, location tracking, weight updates and shipment information, facilitating seamless record sharing among all stakeholders. Every step of the process can be tracked in real time by the project team. The platform can be flexibly configured to adapt to an evolving set of storage locations, with all information and processes being audited.

Distinct roles of excitatory and inhibitory neurons in the medial prefrontal cortex in the expression and reconsolidation of methamphetamine-associated memory in male mice.

Methamphetamine, a commonly abused drug, is known for its high relapse rate. The persistence of addictive memories associated with methamphetamine poses a significant challenge in preventing relapse. Memory retrieval and subsequent reconsolidation provide an opportunity to disrupt addictive memories. However, the key node in the brain network involved in methamphetamine-associated memory retrieval has not been clearly defined. In this study, using the conditioned place preference in male mice, whole brain c-FOS mapping and functional connectivity analysis, together with chemogenetic manipulations of neural circuits, we identified the medial prefrontal cortex (mPFC) as a critical hub that integrates inputs from the retrosplenial cortex and the ventral tegmental area to support both the expression and reconsolidation of methamphetamine-associated memory during its retrieval. Surprisingly, with further cell-type specific analysis and manipulation, we also observed that methamphetamine-associated memory retrieval activated inhibitory neurons in the mPFC to facilitate memory reconsolidation, while suppressing excitatory neurons to aid memory expression. These findings provide novel insights into the neural circuits and cellular mechanisms involved in the retrieval process of addictive memories. They suggest that targeting the balance between excitation and inhibition in the mPFC during memory retrieval could be a promising treatment strategy to prevent relapse in methamphetamine addiction.

Precision agriculture in the United States: A comprehensive meta-review inspiring further research, innovation, and adoption

Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as Scopus® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on ’precision agriculture’ and ’precision farming’, striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices.